The sudden ocean warming and its potential influences on earlyfrozen landfast ice in the Prydz Bay, East Antarctica

The ocean conditions beneath the ice cover play a key role in understanding the sea ice mass balance in the polar regions.An integrated high-frequency ice-ocean observation system,including Acoustic Doppler Velocimeter,Conductivity-Temperature-Depth Sensor,and Sea Ice Mass Balance Array(SIMBA),was deployed in the landfast ice region close to the Chinese Zhongshan Station in Antarctica.A sudden ocean warming of 0.14℃(p<0.01)was observed beneath early-frozen landfast ice,from(−1.60±0.03)℃during April 16-19 to(−1.46±0.07)℃during April 20-23,2021,which is the only significant warming event in the nearly 8-month records.The sudden ocean warming brought a double rise in oceanic heat flux,from(21.7±11.1)W/m^(2) during April 16-19 to(44.8±21.3)W/m^(2) during April 20-23,2021,which shifted the original growth phase at the ice bottom,leading to a 2 cm melting,as shown from SIMBA and borehole observations.Simultaneously,the slowdown of ice bottom freezing decreased salt rejection,and the daily trend of observed ocean salinity changed from+0.02 d^(-1) during April 16-19,2021 to+0.003 d^(-1) during April 20-23,2021.The potential reasons are increased air temperature due to the transit cyclones and the weakened vertical ocean mixing due to the tide phase transformation from semi-diurnal to diurnal.The high-frequency observations within the ice-ocean boundary layer enhance the comprehensive investigation of the ocean’s influence on ice evolution at a daily scale.

Modelling the annual cycle of landfast ice near Zhongshan Station,East Antarctica

A high resolution one-dimensional thermodynamic snow and ice(HIGHTSI)model was used to model the annual cycle of landfast ice mass and heat balance near Zhongshan Station,East Antarctica.The model was forced and initialized by meteorological and sea ice in situ observations from April 2015 to April 2016.HIGHTSI produced a reasonable snow and ice evolution in the validation experiments,with a negligible mean ice thickness bias of(0.003±0.06)m compared to in situ observations.To further examine the impact of different snow conditions on annual evolution of first-year ice(FYI),four sensitivity experiments with different precipitation schemes(0,half,normal,and double)were performed.The results showed that compared to the snow-free case,the insulation effect of snow cover decreased bottom freezing in the winter,leading to 15%–26%reduction of maximum ice thickness.Thick snow cover caused negative freeboard and flooding,and then snow ice formation,which contributed 12%–49%to the maximum ice thickness.In early summer,snow cover delayed the onset of ice melting for about one month,while the melting of snow cover led to the formation of superimposed ice,accounting for 5%–10%of the ice thickness.Internal ice melting was a significant contributor in summer whether snow cover existed or not,accounting for 35%–56%of the total summer ice loss.The multi-year ice(MYI)simulations suggested that when snow-covered ice persisted from FYI to the 10th MYI,winter congelation ice percentage decreased from 80%to 44%(snow ice and superimposed ice increased),while the contribution of internal ice melting in the summer decreased from 45%to 5%(bottom ice melting dominated).

Model simulations of the annual cycle of the landfast ice thickness in the East Siberian Sea

The annual cycle of the thickness and temperature of landfast sea ice in the East Siberian Sea has been examined using a one-dimensional thermodynamic model. The model was calibrated for the year August 2012-July 2013, forced using the data of the Russian weather station Kotel＇ny Island and ECMWF reanalyses. Thermal growth and decay of ice were reproduced well, and the maximum annual ice thickness and breakup day became 1.64 m and the end of July. Oceanic heat flux was 2 W.m^-2 in winter and raised to 25 W.m^-2 in summer, albedo was 0.3-0.8 depending on the surface type （snow/ice and wet/dry）. The model outcome showed sensitivity to the albedo, air temperature and oceanic heat flux. The modelled snow cover was less than 10 cm having a small influence on the ice thickness. In situ sea ice thickness in the East Siberian Sea is rarely available in publications. This study provides a method for quantitative ice thickness estimation by modelling. The result can be used as a proxy to understand the sea ice conditions on the Eurasian Arctic coast, which is important for shipping and high-resolution Arctic climate modelling.

Thickness simulation of landfast ice along Mawson Coast,East Antarctica based on a snow/ice high-resolution thermodynamic model

Landfast ice plays an important role in atmosphere‒ocean interactions and ecosystems in the near coast area of Antarctica.Understanding the characteristics and variations of landfast ice is crucial to the study of climates and field activities in Antarctic.In this study,a high-resolution thermodynamic snow-ice(HIGHTSI)model was applied to simulate the seasonal changes of landfast ice along the Mawson Coast,East Antarctica,through ERA-Interim reanalysis data.Four ocean heat-flux(Fw)values(10,15,20 and 25 W m−2)were used in sensitivity experiments.The results showed that it is reasonable to simulate landfast ice using the HIGHTSI model,and the simulation of landfast ice thickness matched best well with field measurements when Fw was 20 W m^(−2).Then,2-D distributions of landfast ice from 2006 to 2018 were modeled by HIGHTSI with 2-D ERA-Interim reanalysis data in a 0.125°×0.125°cell grid as external forcing.The results showed that fast ice was thicker along the coast and thinner near open water,and usually reaches its maximal thickness in October,varying from 1.2 to 2.0 m through the study area.There was no statistical trend for the thickness during the study period.

Anomalous extensive landfast sea ice in the vicinity of Inexpressible Island, Antarctica

On 10 December 2017,a Chinese research vessel R/V Xuelong encountered an extensive area of landfast ice offshore Inexpressible Island(Antarctica)near the location where the fifth Chinese Antarctic research station is to be built.Using multi-source satellite images and weather data,we analyzed the ice conditions during the event season and reconstructed the development of landfast ice.Two stages in late September and early October were identified as contributing to the final ice extent.These two events are highly related to local-and large-scale weather conditions.Satellite images from 2003 to 2017 showed that four in fifteen years experienced severe landfast ice conditions,suggesting that it is not a rare phenomenon.

Physics of Arctic landfast sea ice and implications on the cryosphere: an overview

Landfast sea ice(LFSI)is a criticalcomponent of the Arctic sea ice cover,and is changing as a result of Arctic amplification of climate change.Located in coastal areas,LFSI is of great significance to the physical and ecological systems of the Arctic shelf and in local indigenous communities.We present an overview of the physics of Arctic LFSI and the associated implications on the cryosphere.LFSI is kept in place by four fastenmechanisms.The evolution of LFSI is mostly determined by thermodynamic processes,and can therefore be usedas an indicator of local climate change.We also present the dynamic processes that are active prior to the formation of LFSI,and those that are involved in LFSI freeze-up and breakup.Season length,thickness and extent of Arctic LFSI are decreasing andshowing different trends in different seas,and therefore,causing environmental and climatic impacts.An improved coordination of Arctic LFSI observation is needed with a unified and systematic observation network supported by cooperation between scientists and indigenous communities,as well as a better application of remote sensing data to acquire detailed LFSI cryosphere physical parameters,hence revolving both its annual cycle and long-term changes.Integrated investigations combining in situ measurements,satellite remote sensing and numerical modeling are needed to improve our understanding of the physical mechanisms of LFSI seasonal changes and their impacts on the environment and climate.